基于高斯过程的量子机器学习
Quantum Machine Learning using Gaussian Processes with Performant Quantum Kernels
摘要：
量子计算机有机会为各种计算任务带来变革。最近，已经提出了使用大型量子设备的不可模拟性来通过使用内核方法执行具有量子优势的回归、分类和其他机器学习任务。虽然不可模拟是机器学习中量子优势的必要条件，但这并不充分，因为并非所有内核都同样有效。在这里，我们研究了使用量子计算机来执行一维和多维回归的机器学习任务，以及使用高斯过程的强化学习。通过使用用额外量子资源增强的高性能经典内核的近似，我们证明了量子设备，无论是在模拟还是在硬件上，都可以执行机器学习任务，至少和经典灵感一样好，甚至好很多倍。我们的内核设计展示了一条有效利用量子设备进行机器学习任务的途径。
英文摘要：
Quantum computers have the opportunity to be transformative for a variety of computational tasks. Recently, there have been proposals to use the unsimulatably of large quantum devices to perform regression, classification, and other machine learning tasks with quantum advantage by using kernel methods. While unsimulatably is a necessary condition for quantum advantage in machine learning, it is not sufficient, as not all kernels are equally effective. Here, we study the use of quantum computers to perform the machine learning tasks of one- and multi-dimensional regression, as well as reinforcement learning, using Gaussian Processes. By using approximations of performant classical kernels enhanced with extra quantum resources, we demonstrate that quantum devices, both in simulation and on hardware, can perform machine learning tasks at least as well as, and many times better than, the classical inspiration. Our informed kernel design demonstrates a path towards effectively utilizing quantum devices for machine learning tasks.
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